جست‌وجوی استاد و پژوهشگر

Veronique Chotteauمشاهده پروفایل

استاد

• Bioprocess Engineering
• Biopharmaceutical Production
• Perfusion Technology
• +۳ مورد دیگر

Veronique Chotteau is a Professor and Principal Investigator of the Cell Technology group at KTH Royal Institute of Technology's Department of Industrial Biotechnology. She leads the VINNOVA Competence Centre for Advanced BioProduction (AdBIOPRO), focusing on continuous bioprocessing of mammalian cells for biopharmaceuticals, viral vectors, and therapeutic cells. Her research emphasizes perfusion technology, mathematical modeling (kinetic and metabolic flux analysis), and process optimization. Active in projects like 'Data-Limited Learning of Complex Dynamical Systems,' she explores AI-driven bioprocess control. She directs AdBIOPRO’s efforts in high-cell-density perfusion and continuous manufacturing. Her work integrates bioreactor design, process intensification, and novel separation technologies (e.g., magnetic beads). Teaching includes courses on animal cell culture, drug development, and systems analysis. Over 50 peer-reviewed publications span bioprocess modeling, metabolic engineering, and bioreactor optimization. Her lab develops tools like microfluidic cartridges and Raman-based analytics for real-time process monitoring. Key collaborations include Digital Futures (a KTH-Stockholm University-RISE initiative) and industry partners for translational bioprocess solutions. Her research addresses challenges in scalable production of complex biologics under data-limited conditions.

Ivy Pengمشاهده پروفایل

دانشیار

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Computing
• +۶ مورد دیگر

Ivy Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology’s School of Electrical Engineering and Computer Science (EECS), where she leads the Scalable Computing Laboratory (ScaLab). She also holds a Docent title in Parallel Computing and is affiliated with the Digital Futures Faculty and the Cooperate Working Group. Prior to Sweden, she worked at the Lawrence Livermore National Laboratory (LLNL) as a Computer Scientist and as a post-doctoral fellow at Oak Ridge National Laboratory (ORNL) in the USA. Her research focuses on large-scale parallel systems, system-level optimization through workload and architecture awareness, performance modeling and analysis, and memory subsystem optimization. Key areas include converged HPC and cloud computing, heterogeneous accelerators (e.g., GPUs, RISC-V Vector units, QPUs, DPUs), and heterogeneous memories (e.g., persistent memory, HBM). She also explores disaggregated resources to enhance computing efficiency and scalability. Ivy Peng coordinates the EU Horizon 2022 Project OpenCUBE, which develops open-source cloud-based services for EPI systems, and leads the Swedish Research Council (VR) Grant on Disaggregated Memory for Emerging Parallel Systems. She actively contributes to technical committees and review boards for journals like IEEE TPDS and conferences including SC, ICS, HPDC, and IPDPS. She advises Ph.D. students Jacob Wahlgren, Gabin Schieffer, Ruimin Shi, and Daniel Medeiros, while co-supervising Jeremy Williams and Måns Andersson. Her academic leadership extends to multiple courses at KTH, including Applied GPU Programming, Introduction to High Performance Computing, and Quantum Computing for Computer Scientists.

Jiaojiao Zhangمشاهده پروفایل

پژوهشگر ارشد

• Distributed Optimization
• Decentralized Machine Learning
• Algorithm Design
• +۴ مورد دیگر

Jiaojiao Zhang is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology, focusing on decentralized machine learning algorithms with convergence guarantees. Her research addresses second-order, online, and robust methods for distributed optimization, emphasizing applications in privacy-preserving technologies and smart systems. Education: B.E. in Automation (Harbin Engineering University, 2015), M.Sc. in Control Theory (University of Science and Technology of China, 2018), Ph.D. in Operations Research (CUHK, 2022). Award: Hong Kong PhD Fellowship Scheme (2018). Supervisors: Mikael Johansson (KTH EECS) and Joakim Jaldén (KTH). Her work bridges distributed optimization and algorithm design, targeting challenges in federated learning, wireless sensor networks, and multi-core processor training. Projects like 'Decentralized Learning' aim to enhance cooperation and trust in digital futures through robust, efficient algorithms. Key areas: privacy-preserving federated learning, Byzantine-robust systems, and dynamic privacy allocation. Publications emphasize convergence analysis, communication efficiency, and adversarial resilience in decentralized settings. She actively contributes to Digital Futures, a joint initiative of KTH, Stockholm University, and RISE.

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Systems Optimization
• +۵ مورد دیگر

Bo Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology in Stockholm, leading the Scalable Computing Laboratory (ScaLab). Previously, he held roles as a Computer Scientist at Lawrence Livermore National Laboratory (LLNL) and a postdoctoral fellow at Oak Ridge National Laboratory (ORNL). He coordinates the EU Horizon 2022 Project OpenCUBE and leads a Swedish Research Council (VR) grant on Disaggregated Memory for Emerging Parallel Systems. His research focuses on optimizing large-scale parallel systems through architecture-aware design, performance modeling, and memory subsystem innovation for heterogeneous computing environments. He teaches courses such as Applied GPU Programming, High-Performance Computing, and Operating Systems. As an academic leader, he supervises multiple PhD students and co-supervises additional researchers. His service includes roles on technical program committees for top conferences like IEEE TPDS, SC, and ICS, and journals like IEEE Transactions on Parallel and Distributed Systems. Bo Peng’s work has been recognized with prestigious awards, including the R&D 100 Award in 2024. His research bridges HPC and cloud computing, emphasizing scalable systems and resource efficiency in next-generation architectures.

Tobias Langerمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Operating Systems
• Parallel and Distributed Processing
• +۳ مورد دیگر

Tobias Langer is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His affiliation includes the Technische Fakultät and Department Informatik. He focuses on invasive computing systems, runtime support systems, and distributed architectures. Research interests include distributed systems, operating systems, parallel computing, and real-time systems. His work emphasizes runtime systems for many-core architectures, resource arbitration, and virtual shared memory solutions for MPSoCs. Recent publications explore iRTSS (invasive runtime support system) and system software for future computing architectures. His research trends address scalability, resource management, and real-time challenges in modern computing environments. No scientific awards are explicitly mentioned. He has advised multiple students on topics like OctoPOS operating system development and system monitoring units. Langer's work is affiliated with the SFB/TRR 89 Invasive Computing project and contributes to the OctoPOS kernel development. His lab is part of CS 4 at FAU, located in Room 0.041-113.

Jens Schedelمشاهده پروفایل

پژوهشگر

• Distributed Systems
• Operating Systems
• Invasive Computing
• +۳ مورد دیگر

Jens Schedel is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander University Erlangen-Nuremberg. His academic activities focus on invasive computing, distributed systems, and operating systems. He is affiliated with the Faculty of Engineering and collaborates with the ergoo group (Erlangen Research Group on Distributed Objects and Operating Systems). Education: 2010: Diploma Thesis: 'Design and implementation of a system abstraction layer for the I4Copter framework (QC_OSAL)' 2009: Study Thesis: 'Design and implementation of a distributed management and control software for projectors' Research Interests: Invasive Runtime Support Systems (iRTSS), aspect-oriented operating systems (CiAO), interdisciplinary quadrocopter projects (I4Copter), resource-aware programming, and embedded systems. His work emphasizes adaptive algorithms, parallel computing, and type-safe migration in safety-critical environments. Teaching: Taught courses (SP1/SP2) across multiple semesters (2010–2015), including FITS (Formal IT Systems) and PASST (Programming and Software Systems). Labs/Teams: Member of the ergoo group and contributes to the SFB/TRR 89 Invasive Computing project.

Benjamin Oechsleinمشاهده پروفایل

پژوهشگر

• Operating Systems
• Distributed Systems
• Real-Time Systems
• +۴ مورد دیگر

Benjamin Oechslein is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems Group) at the University of Erlangen-Nuremberg, part of the Faculty of Engineering. He holds a Diplom-Informatik (Master's equivalent) from the same university, completing his thesis titled Statische WCET Analyse von LLVM Bytecode in 2008 under Prof. Wolfgang Schröder-Preikschat and Dr. Fabian Scheler. His research focuses on invasive computing, real-time systems, parallel operating systems, and resource-aware algorithms for embedded and many-core systems. He has contributed to projects like iRTSS (invasive Runtime Support System) within the SFB/TRR 89 Invasive Computing initiative. Teaching activities include leading exercises and labs for courses such as Betriebssysteme , Betriebssystemtechnik , and Systemnahe Programmierung in C across multiple academic terms from 2009 to 2014. His work spans theoretical and applied aspects of distributed systems, with publications in journals like Journal of Systems Architecture and conferences such as ARCS and CASES. Key technical contributions include developing the OctoPOS parallel operating system and advancing resource-aware algorithms for heterogeneous MPSoCs. His research emphasizes scalability and real-time performance in many-core environments, with applications in image processing and embedded systems.

Sai Qian Zhangمشاهده پروفایل

استادیار

• Efficient AI Algorithm
• AR/VR Computing
• Neural Network Accelerator Design
• +۲ مورد دیگر

Sai Qian Zhang is an Assistant Professor at New York University, holding dual appointments in the Electrical and Computer Engineering Department at NYU Tandon School of Engineering and the Computer Science Department at the Courant Institute of Mathematical Sciences. He earned his Ph.D. from Harvard University in 2021 and completed his B.A.Sc and M.A.Sc at the University of Toronto. Previously, he worked as a Senior Research Scientist at Meta Reality Labs (2022–2024). His research focuses on algorithm and hardware co-design for efficient deep neural network implementation, with a particular emphasis on AR/VR computing. Key areas include neural network accelerator design, efficient AI algorithms, and privacy-preserving techniques for AR/VR systems. He leads the System & Artificial Intelligence (SAI) Lab, which explores intersections between deep learning and hardware systems to optimize AI performance and resource efficiency. Recent research highlights include papers on gaze-tracked foveated rendering, speculative decoding strategies, and parameter-efficient fine-tuning for large models. He has advised numerous students, including recipients of prestigious awards such as the DAC Young Fellow and ECE Myron M. Rosenthal Award. Zhang teaches courses like Efficient AI and Hardware Accelerator Design (Spring 2025), emphasizing model compression, quantization, and accelerator architectures. His work has been recognized through grants and collaborations with industry partners like Meta and Andes Technology.

• High-Performance Computing
• Graph Algorithms
• Social Network Analysis
• +۳ مورد دیگر

Johannes Langguth is an Associate Professor at the Department of Informatics, University of Bergen. His work focuses on high-performance computing, graph algorithms, and social network analysis. He has contributed to GPU acceleration techniques for cardiac simulations and graph processing on manycore architectures. Langguth's research intersects computational science with societal challenges, including misinformation detection in social media and conspiracy theory analysis. He leads projects on temporal interaction networks and has developed datasets like COCO and GECO to study pandemic-related disinformation. His interdisciplinary approach combines computer science with psychology and public health, addressing both technical and human aspects of information dissemination. Notable contributions include optimizing breadth-first search on Graphcore IPUs and applying graph neural networks for combinatorial problems. Langguth collaborates widely, with affiliations at Simula Research Laboratory and BI Norwegian School of Business. His work is funded by institutions like the Research Council of Norway and the EU, reflecting its applied and impactful nature in both academia and industry.

Huaiyu Daiمشاهده پروفایل

استاد

• Networking
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Huaiyu Dai is a Professor and University Faculty Scholar at the Department of Electrical and Computer Engineering, North Carolina State University. He holds editorial roles including Editor-in-Chief of IEEE Transactions on Signal and Information Processing over Networks. His research focuses on networking, machine learning, and communications, with over 300 publications. He has received IEEE Fellowships, AAIA Fellowships, and awards like the Qualcomm Faculty Award and William R. Bennett Prize. Education : Ph.D., Electrical Engineering, Princeton University (2002) M.S., Electrical Engineering, Tsinghua University (1998) B.E., Electrical Engineering, Tsinghua University (1996) Research Interests : His work spans federated learning, edge computing, network security, and graph neural networks. Recent advancements include decentralized federated learning frameworks, privacy-preserving techniques, and efficient scheduling algorithms for vehicular/cloud networks. Publications Trends : Recent articles address challenges in federated learning (data heterogeneity, privacy), edge computing (resource optimization), and UAV-enabled systems (fresh data collection, trajectory planning). Methodologies include reinforcement learning, coded computing, and graph-based approaches. Awards & Leadership : AAIA Fellow (2021) IEEE Fellow (2017) Multiple best paper awards at IEEE ICC, INFOCOM, and MASS Advising & Grants : Active in mentoring graduate students and securing grants for distributed machine learning, vehicular networks, and secure communication systems. Leads projects on hybrid defense mechanisms against Byzantine attacks in federated learning. Labs/Teams : Collaborates through interdisciplinary teams at NC State, focusing on AI-driven networking, edge computing architectures, and UAV communication systems.

• Radiation Hardening
• Fault-Tolerant Systems
• Microelectronics
• +۵ مورد دیگر

Mario Garcia Valderas is an Associate Professor at the Department of Electronic Technology, Universidad Carlos III de Madrid. He holds roles as Coordinator of the Electronic Technology program and Deputy Director of Infrastructure and Laboratories. His research focuses on radiation-hardened systems, fault-tolerant architectures, and embedded systems for aerospace and space applications. His work emphasizes reliability in microelectronics and FPGA-based solutions, addressing challenges like single-event effects in space and high-reliability computing. Key areas include RISC-V processors, heterogeneous computing, and hardware-software co-design for planetary exploration instruments. Publications highlight advancements in radiation-hardening techniques, error detection/recovery architectures, and fault-injection methodologies. Over 100 peer-reviewed articles and book chapters evidence his contributions to microelectronics and embedded systems reliability. He leads the Microelectronic Design and Applications (DMA) research group and collaborates with institutions like CNA for combined irradiation testing. His research spans from theoretical design hardening to practical implementations in aerospace-grade electronics.

• Recommender Systems
• Artificial Intelligence
• Data Mining
• +۶ مورد دیگر

Dr. Yongli Ren is an Associate Professor in the School of Computing Technologies at RMIT University, located at the City Campus in Australia. His research focuses on advancing Recommender Systems, with particular emphasis on fairness, quantum computing optimization, and spatio-temporal data analysis. He holds expertise in data mining, collaborative filtering, and context-aware systems. Dr. Ren is open to supervising Masters and PhD students in areas such as quantum annealer-based optimization, fairness-aware recommendation, and heterogeneous time-series analysis. His teaching interests include web search algorithms, collaborative filtering, data mining methodologies, and log analysis. Dr. Ren has contributed to over 70 research outputs, with recent works addressing recommendation system evaluation metrics, fairness in AI, and energy-efficient routing protocols in wireless sensor networks. His research has been published in prestigious venues like ACM Transactions on Information Systems, IEEE Access, and the ACM Web Conference (WWW). Key research themes include addressing the precision-diversity trade-off in recommendations, enhancing fairness through dual-temporal networks, and leveraging quantum computing for QUBO optimization. He has also explored applications in mobility pattern analysis, workplace productivity systems, and fog-based distributed recommendations.

Timothy Moroneyمشاهده پروفایل

استاد

• Computational Mathematics
• High Performance Computing
• Surface Reconstruction
• +۴ مورد دیگر

Professor Timothy Moroney holds the position of Professor in Mathematics at Queensland University of Technology (QUT), within the School of Mathematical Sciences. His research focuses on computational mathematics, high-performance computing, and their applications in fluid dynamics, agrichemical retention, and fractional diffusion phenomena. He earned his PhD from QUT and has contributed extensively to interdisciplinary projects including surface reconstruction from 3D laser scans for agricultural applications, droplet evaporation modeling on plant surfaces, and ship wake pattern analysis. His work bridges theoretical mathematics with practical engineering solutions. Key research areas include: Surface reconstruction using scattered data interpolation Mathematical modeling of droplet dynamics on heterogeneous surfaces Time-frequency analysis of ship-generated wave patterns Distributed-order fractional differential equations for nonlocal diffusion He has secured multiple Australian Competitive Grants, including projects on fractional models, ship wakes, and agrichemical retention. His awards include the Vice-Chancellor's Performance Award (2016) and Senior Fellow of the Higher Education Academy (2017). Teaching responsibilities include foundational mathematics units focused on computational problem-solving and linear algebra applications in big data.

• Computer Architecture
• High-Performance Computing
• Network-on-Chip (NoC)
• +۷ مورد دیگر

Vassilis D. Papaefstathiou is a researcher in computer architecture and high-performance computing, affiliated with the Department of Computer Science at the University of Crete and the Institute of Computer Science at FORTH-ICS. His work focuses on energy-efficient manycore systems, network-on-chip design, FPGA prototyping, and RDMA-based communication. His educational background includes a Ph.D. (2013), M.Sc. (2005), and B.Sc. (2002), all from the University of Crete. His research spans advanced topics in computer systems, including hybrid memory management, cache optimization, and scalable interconnects. His research interests include Computer Architecture , Network-on-Chip (NoC) , Reconfigurable Computing , Energy-Efficient Computing , RDMA , and High-Performance Computing . He has made significant contributions to FPGA-based prototyping of manycore systems and low-latency interconnects. His recent publications demonstrate a strong trend in optimizing on-chip networks, memory hierarchies, and communication mechanisms for performance and energy efficiency. Key areas include dual data-rate NoCs, hybrid memory systems with intelligent data migration, and RDMA-enhanced architectures. His work often integrates hardware-software co-design principles for scalable and efficient computing. HiPEAC Paper Award (2012) HiPEAC Paper Award (2016) HiPEAC Paper Award (2017) HiPEAC Paper Award (2020) Best Paper Award Finalist at IEEE/ACM NOCS 2018 He has advised several researchers and students, including Antonis Psistakis, Evangelos Vasilakis, and Ahsen Ejaz, who have co-authored significant publications with him. His collaborative projects include SARC, ExaNeSt, and ECOSCALE, often funded by EU initiatives or research councils. These projects focus on next-generation HPC systems, reconfigurable computing, and scalable architectures. He is a key contributor to the Formic FPGA prototyping platform and has worked extensively on RDMA-capable NICs, cache-integrated network interfaces, and virtualized communication systems. His work is deeply embedded in international research consortia and high-impact venues.

Jorge Fernández-Berniمشاهده پروفایل

دانشیار

• Smart CMOS image sensors
• Vision chips
• Embedded systems
• +۱۰ مورد دیگر

Jorge Fernández-Berni is an Associate Professor at the University of Seville, affiliated with the Institute of Microelectronics of Seville (IMSE-CNM), a joint research center of CSIC and the university. He holds a PhD in Microelectronics (2011, with honors) and has been a faculty member since 2018, following a Juan de la Cierva Research Fellowship (2016–2017). Research Interests: Smart CMOS image sensors Vision chips Embedded systems and hardware-software co-design Distributed smart sensors Deep learning and edge AI Nature and biodiversity monitoring His recent publications reflect a strong trend toward integrating AI with low-power embedded vision systems for ecological applications, particularly in wildlife and environmental monitoring. The work emphasizes efficient, real-time processing on edge devices, focal-plane computation, and system-level optimization for IoT deployment. Topics include high dynamic range imaging, Gaussian pyramid extraction, thermal throttling impact, and performance prediction for deep learning models on embedded platforms. Scientific Awards: Best Paper Award, SPIE Electronic Imaging 2014 Third Prize, Student Paper Award, IEEE CNNA 2010 Winner, 'De Idea a Producto' contest 2022 HiPEAC 2023 Technology Transfer Award Winner, Startup Olé Marbella 2024 Student Pitch Competition Advising and Grants: Dr. Fernández-Berni has served as Principal Investigator (PI) in numerous research and tech transfer projects, including ULTIMATE (smart embedded platform for nature monitoring), SEMIoTICS (intelligent IoT components), and collaborations with SEO/BirdLife on automated bird identification. He has participated in approximately 25 research projects, with funding from national agencies (Ministerio de Ciencia e Innovación, Junta de Andalucía), the European Union (H2020), and the U.S. Office of Naval Research. He has also led the spin-off BiodAIverse (now biotfy), which has received recognition and acceleration support. Labs and Teams: He is a core researcher at the Institute of Microelectronics of Seville (IMSE-CNM), where he contributes to advanced sensor development and participates in the Doctoral Program on Physical Sciences and Technologies. He has collaborated extensively with the research group led by Á. Rodríguez-Vázquez and R. Carmona-Galán, focusing on smart vision systems and embedded processing.